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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/default_configs.db +0 -0
  8. .config/gce +1 -0
  9. .config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  10. .config/logs/2025.06.17/13.36.10.034228.log +765 -0
  11. .config/logs/2025.06.17/13.36.32.451069.log +5 -0
  12. .config/logs/2025.06.17/13.36.41.886635.log +153 -0
  13. .config/logs/2025.06.17/13.36.43.880673.log +5 -0
  14. .config/logs/2025.06.17/13.36.53.766289.log +8 -0
  15. .config/logs/2025.06.17/13.36.54.470285.log +8 -0
  16. .gitattributes +7 -0
  17. checkpoints/model/eval/similarity_evaluation_sts-eval_results.csv +4 -0
  18. checkpoints/model/runs/Jun19_19-44-11_d58992483850/events.out.tfevents.1750362252.d58992483850.880.0 +3 -0
  19. checkpoints/model_1/runs/Jun19_20-21-31_d58992483850/events.out.tfevents.1750364493.d58992483850.880.1 +3 -0
  20. rahul-sbert-finetuned/1_Pooling/config.json +10 -0
  21. rahul-sbert-finetuned/README.md +446 -0
  22. rahul-sbert-finetuned/config.json +25 -0
  23. rahul-sbert-finetuned/config_sentence_transformers.json +10 -0
  24. rahul-sbert-finetuned/model.safetensors +3 -0
  25. rahul-sbert-finetuned/modules.json +20 -0
  26. rahul-sbert-finetuned/sentence_bert_config.json +4 -0
  27. rahul-sbert-finetuned/special_tokens_map.json +37 -0
  28. rahul-sbert-finetuned/tokenizer.json +0 -0
  29. rahul-sbert-finetuned/tokenizer_config.json +65 -0
  30. rahul-sbert-finetuned/vocab.txt +0 -0
  31. rahul-sbert-model/1_Pooling/config.json +10 -0
  32. rahul-sbert-model/README.md +446 -0
  33. rahul-sbert-model/config.json +25 -0
  34. rahul-sbert-model/config_sentence_transformers.json +10 -0
  35. rahul-sbert-model/model.safetensors +3 -0
  36. rahul-sbert-model/modules.json +20 -0
  37. rahul-sbert-model/sentence_bert_config.json +4 -0
  38. rahul-sbert-model/special_tokens_map.json +37 -0
  39. rahul-sbert-model/tokenizer.json +0 -0
  40. rahul-sbert-model/tokenizer_config.json +65 -0
  41. rahul-sbert-model/vocab.txt +0 -0
  42. sample_data/README.md +19 -0
  43. sample_data/anscombe.json +49 -0
  44. sample_data/california_housing_test.csv +0 -0
  45. sample_data/california_housing_train.csv +0 -0
  46. sample_data/mnist_test.csv +3 -0
  47. sample_data/mnist_train_small.csv +3 -0
  48. sentence-transformer-indonesia-final/1_Pooling/config.json +10 -0
  49. sentence-transformer-indonesia-final/README.md +384 -0
  50. sentence-transformer-indonesia-final/config.json +25 -0
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.config/configurations/config_default ADDED
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+ 2025-06-17 13:36:23,530 INFO ___FILE_ONLY___ ╠═ Downloading: Bundled Python 3.12 (Platform Specific) ═╣
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+ 2025-06-17 13:36:23,740 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bundled-python3-unix-linux-x86_64-20250502143716.tar.gz HTTP/11" 200 93610468
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+ 2025-06-17 13:36:24,389 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250418150427.tar.gz HTTP/11" 200 12382702
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+ 2025-06-17 13:36:24,460 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
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+ 2025-06-17 13:36:24,509 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20250523104322.tar.gz HTTP/11" 200 1950
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+ 2025-06-17 13:36:24,514 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
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+ 2025-06-17 13:36:24,723 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/11" 200 2325
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+ 2025-06-17 13:36:24,937 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250613150750.tar.gz HTTP/11" 200 1525557
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+ 2025-06-17 13:36:24,955 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
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+ 2025-06-17 13:36:24,958 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-06-17 13:36:25,217 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/11" 200 104
482
+ 2025-06-17 13:36:25,217 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+
487
+ 2025-06-17 13:36:25,220 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
489
+ 2025-06-17 13:36:25,220 INFO ___FILE_ONLY___ ╚
490
+ 2025-06-17 13:36:25,312 INFO ___FILE_ONLY___ ═
491
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492
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500
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502
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
+ 2025-06-17 13:36:25,438 INFO ___FILE_ONLY___ ═
545
+ 2025-06-17 13:36:25,440 INFO ___FILE_ONLY___ ═
546
+ 2025-06-17 13:36:25,442 INFO ___FILE_ONLY___ ═
547
+ 2025-06-17 13:36:25,444 INFO ___FILE_ONLY___ ═
548
+ 2025-06-17 13:36:25,446 INFO ___FILE_ONLY___ ═
549
+ 2025-06-17 13:36:25,448 INFO ___FILE_ONLY___ ═
550
+ 2025-06-17 13:36:25,448 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-06-17 13:36:25,455 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-06-17 13:36:25,456 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-06-17 13:36:25,456 INFO ___FILE_ONLY___ ╚
557
+ 2025-06-17 13:36:25,456 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-06-17 13:36:25,456 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-06-17 13:36:25,462 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-06-17 13:36:25,462 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-06-17 13:36:25,462 INFO ___FILE_ONLY___ ╚
565
+ 2025-06-17 13:36:25,465 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-06-17 13:36:25,465 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-06-17 13:36:25,466 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-06-17 13:36:25,466 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-06-17 13:36:25,466 INFO ___FILE_ONLY___ ╚
573
+ 2025-06-17 13:36:27,689 INFO ___FILE_ONLY___ ═
574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
+ 2025-06-17 13:36:30,534 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-06-17 13:36:30,586 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-06-17 13:36:30,586 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-06-17 13:36:30,586 INFO ___FILE_ONLY___ ╚
640
+ 2025-06-17 13:36:31,106 INFO ___FILE_ONLY___ ═
641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
+ 2025-06-17 13:36:31,662 INFO ___FILE_ONLY___ ═
687
+ 2025-06-17 13:36:31,671 INFO ___FILE_ONLY___ ═
688
+ 2025-06-17 13:36:31,682 INFO ___FILE_ONLY___ ═
689
+ 2025-06-17 13:36:31,694 INFO ___FILE_ONLY___ ═
690
+ 2025-06-17 13:36:31,707 INFO ___FILE_ONLY___ ═
691
+ 2025-06-17 13:36:31,720 INFO ___FILE_ONLY___ ═
692
+ 2025-06-17 13:36:31,732 INFO ___FILE_ONLY___ ═
693
+ 2025-06-17 13:36:31,745 INFO ___FILE_ONLY___ ═
694
+ 2025-06-17 13:36:31,757 INFO ___FILE_ONLY___ ═
695
+ 2025-06-17 13:36:31,771 INFO ___FILE_ONLY___ ═
696
+ 2025-06-17 13:36:31,786 INFO ___FILE_ONLY___ ═
697
+ 2025-06-17 13:36:31,806 INFO ___FILE_ONLY___ ═
698
+ 2025-06-17 13:36:31,822 INFO ___FILE_ONLY___ ═
699
+ 2025-06-17 13:36:31,839 INFO ___FILE_ONLY___ ═
700
+ 2025-06-17 13:36:31,839 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-06-17 13:36:31,868 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-06-17 13:36:31,869 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-06-17 13:36:31,869 INFO ___FILE_ONLY___ ╚
707
+ 2025-06-17 13:36:31,869 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-06-17 13:36:31,870 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-06-17 13:36:31,874 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-06-17 13:36:31,874 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-06-17 13:36:31,874 INFO ___FILE_ONLY___ ╚
715
+ 2025-06-17 13:36:31,876 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-06-17 13:36:31,876 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-06-17 13:36:31,878 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-06-17 13:36:31,878 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-06-17 13:36:31,878 INFO ___FILE_ONLY___ ╚
723
+ 2025-06-17 13:36:31,879 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-06-17 13:36:31,879 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-06-17 13:36:31,879 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-06-17 13:36:31,884 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-06-17 13:36:31,884 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-06-17 13:36:31,884 INFO ___FILE_ONLY___ ╚
732
+ 2025-06-17 13:36:31,886 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-06-17 13:36:31,886 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-06-17 13:36:31,888 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-06-17 13:36:31,888 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-06-17 13:36:31,888 INFO ___FILE_ONLY___ ╚
740
+ 2025-06-17 13:36:31,927 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-06-17 13:36:31,928 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-06-17 13:36:31,928 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-06-17 13:36:31,933 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-06-17 13:36:31,933 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-06-17 13:36:31,933 INFO ___FILE_ONLY___ ╚
749
+ 2025-06-17 13:36:31,933 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-06-17 13:36:31,933 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-06-17 13:36:31,938 DEBUG root Updating notification cache...
753
+ 2025-06-17 13:36:31,939 INFO ___FILE_ONLY___
754
+
755
+ 2025-06-17 13:36:31,940 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-06-17 13:36:31,941 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-06-17 13:36:40,982 DEBUG ___FILE_ONLY___
758
+ 2025-06-17 13:36:40,982 DEBUG ___FILE_ONLY___
759
+ 2025-06-17 13:36:41,246 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-06-17 13:36:41,246 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-06-17 13:36:41,249 DEBUG root Chosen display Format:none
765
+ 2025-06-17 13:36:41,249 INFO root Display format: "none"
.config/logs/2025.06.17/13.36.32.451069.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-06-17 13:36:32,451 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-06-17 13:36:32,453 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-06-17 13:36:32,455 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-06-17 13:36:40,866 DEBUG root Chosen display Format:none
5
+ 2025-06-17 13:36:40,866 INFO root Display format: "none"
.config/logs/2025.06.17/13.36.41.886635.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-06-17 13:36:41,887 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-06-17 13:36:41,889 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
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+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:200
8
+ - loss:TripletLoss
9
+ base_model: sentence-transformers/all-MiniLM-L6-v2
10
+ widget:
11
+ - source_sentence: '[ELISTA] Apa saja kegiatan utama yang dikelola oleh ELISTA? (Tugas
12
+ Akhir/Skripsi/Tesis/Disertasi)?'
13
+ sentences:
14
+ - Pengajuan judul tugas akhir.Pembuatan alur waktu (timeline) tugas akhir.Pembimbingan
15
+ secara online dan offline.Pembuatan undangan dan jadwal seminar/sidang tugas akhir.Manajemen
16
+ tugas akhir untuk pengelola tugas akhir.Informasi terkait mahasiswa bimbingan,
17
+ serta rekap dosen pembimbing, dosen penguji, dan mahasiswa bimbingan.Berbagai
18
+ informasi bagi dosen seperti jadwal konsultasi, jadwal seminar/sidang dan lainnya.Berbagai
19
+ informasi bagi mahasiswa seperti penerimaan/penolakan ajuan judul tugas akhir,
20
+ respon bimbingan oleh dosen, dan lainnya. Pengajuan judul tugas akhir. Pembuatan
21
+ alur waktu (timeline) tugas akhir. Pembimbingan secara online dan offline. Pembuatan
22
+ undangan dan jadwal seminar/sidang tugas akhir. Manajemen tugas akhir untuk pengelola
23
+ tugas akhir. Informasi terkait mahasiswa bimbingan, serta rekap dosen pembimbing,
24
+ dosen penguji, dan mahasiswa bimbingan. Berbagai informasi bagi dosen seperti
25
+ jadwal konsultasi, jadwal seminar/sidang dan lainnya. Berbagai informasi bagi
26
+ mahasiswa seperti penerimaan/penolakan ajuan judul tugas akhir, respon bimbingan
27
+ oleh dosen, dan lainnya.
28
+ - 'Jumlah Poin maksimal kelebihan kinerja yang dapat dibayarkan:Untuk Dosen biasa
29
+ = 56 poin (150%)Untuk Dosen dengan tugas tambahan = 42 poin (150%) Jumlah Poin
30
+ maksimal kelebihan kinerja yang dapat dibayarkan: Untuk Dosen biasa = 56 poin
31
+ (150%) Untuk Dosen dengan tugas tambahan = 42 poin (150%)'
32
+ - FOTOCOPY KONVERSI NIPFOTOCOPY SK CPNSFOTOCOPY SK PNSFOTOCOPY SK PANGKAT TERAKHIRDAFTAR
33
+ KELUARGA PNS (DIKETAHUI ATASAN LANGSUNG)LAPORAN PERKAWINAN PERTAMA (DIKETAHUI
34
+ ATASAN LANGSUNG)FOTOCOPY SURAT NIKAH YANG DI LEGALISIR OLEH ATASAN LANGSUNGPAS
35
+ FOTO UKURAN 3X4 (HITAM PUTIH) 3 LEMBAR (SUAMI+ISTRI)LAPORAN PERKAWINAN KEDUA (DIKETAHUI
36
+ ATASAN LANGSUNG)*
37
+ - source_sentence: Bagaimana cara mendapatkan Surat Keterangan Sedang Tidak Menerima
38
+ Beasiswa?
39
+ sentences:
40
+ - Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan
41
+ menggunakan akun yang sama dengan akun akses ke SIAKAD. Anda dapat mengakses atau
42
+ login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama
43
+ dengan akun akses ke SIAKAD.
44
+ - 1. Menemui Wakil Dekan III Bidang Kemahasiswaan dan Alumni Keguruan dan Ilmu Pendidikanguna
45
+ mendapatkan Memomengenai hal terkait2. Membawa memo tersebut pada bagian Kemahasiswaan
46
+ FKIP 1. Menemui Wakil Dekan III Bidang Kemahasiswaan dan Alumni Keguruan dan Ilmu
47
+ Pendidikanguna mendapatkan Memomengenai hal terkait 2. Membawa memo tersebut pada
48
+ bagian Kemahasiswaan FKIP
49
+ - Tidak boleh dihapus, tapi ajukan permohonan perbaikan/penyesuaian MK pada kurikulum
50
+ melalui Dekan/Wakil Dekan Akademik kemudian disampaikan kepada Wakil Rektor Bidang
51
+ Akademik. Tidak boleh dihapus, tapi ajukan permohonan perbaikan/penyesuaian MK
52
+ pada kurikulum melalui Dekan/Wakil Dekan Akademik kemudian disampaikan kepada
53
+ Wakil Rektor Bidang Akademik.
54
+ - source_sentence: bagaimana prosedur pembuatan surat keterangan lulus (SKL)?
55
+ sentences:
56
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
57
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
58
+ 16 sks.
59
+ - Surat Keterangan Lulus (SKL) dapat diajukan apabila mahasiswa telah mendaftar
60
+ yudisium fakultas.1. Mahasiswa mengajukan surat permohonan yang ditujukan ke Dekan
61
+ Fakultas Sains dan Teknologi2. Melampirkan berita acara ujian skripsi dan transkrip
62
+ nilai sementara Surat Keterangan Lulus (SKL) dapat diajukan apabila mahasiswa
63
+ telah mendaftar yudisium fakultas. 1. Mahasiswa mengajukan surat permohonan yang
64
+ ditujukan ke Dekan Fakultas Sains dan Teknologi 2. Melampirkan berita acara ujian
65
+ skripsi dan transkrip nilai sementara
66
+ - Password simpeg sama dengan password siakad,bila password siakad pun lupa silahkan
67
+ datang ke helpdesk LPTIK atau kirim email ke [email protected] Password
68
+ simpeg sama dengan password siakad, bila password siakad pun lupa silahkan datang
69
+ ke helpdesk LPTIK atau kirim email ke [email protected]
70
+ - source_sentence: Jika asesor pertama menyatakan valid, tapi asesor kedua menyatakan
71
+ tidak valid terhadap BKD saya, apa yang harus saya lakukan (Remunerasi)?
72
+ sentences:
73
+ - Perhatikan saran/komentar dari asesor kedua dan kemudian tindaklanjuti sesuai
74
+ saran/komentar dari asesor kedua. Perhatikan saran/komentar dari asesor kedua
75
+ dan kemudian tindaklanjuti sesuai saran/komentar dari asesor kedua.
76
+ - 'Untuk pelayanan mahasiswa yang berkaitan dengan akademik dapat menghubungi staf
77
+ kependidikan masing-masing jurusan pada program studi, atau bisa menghubungi kontak
78
+ person sebagai berikut:# Jurusan Pendidikan Dokter1.  Program Studi Pendidikan
79
+ Dokter : Ibu Melly Priyana, SE (085208500620) Ruang Kerja : Ruang Akademik FKIK
80
+ Unja2. Program Studi Profesi Dokter : Ibu Yuricho,S.Kep (085269962998) Ruang Kerja
81
+ :Ruang Dekan FKIK Unja# Jurusan Ilmu Keperawatan1. Program Studi  Ilmu Keperawatan
82
+ : Ibu Nursamsiah (082178444274) Ruang kerja : Ruang Akademik FKIK Unja2. Program
83
+ Studi Profesi Ners : Ibu Nurlaila,S.Pd (082177180778)Ruang kerja : Ruang Akademik
84
+ FKIK Unja# Jurusan Ilmu Kesehatan Masyarakat1. Program Studi Ilmu Kesehatan Masyarakat
85
+ : Bapak Yudhi Desfratama Putra Alda, S.H. (085839603772) Ruang kerja : Ruang Program
86
+ Studi Ilmu Kesehatan Masyarakat# Jurusan Ilmu Psikolgi1. Program Studi Psikologi
87
+ : Ibu Lina (085383337698) Ruang kerja : Ruang Dekan FKIK Unja# Jurusan Farmasi1.
88
+ Program Studi Farmasi : Bapak Ides Nawinata, S.E (085269826454) Ruang kerja :
89
+ Ruang Perlengkapan FKIK Unja# Kemahasiswaan (Aktif Kuliah)1. Kemahasiswaan : Bapak
90
+ Irwansyah, S.K.M (0895620070754) Ruang kerja : Ruang Perlengkapan FKIK Unja# Laboratorium1.
91
+ Lab Keterampilan Klinik (SKillab) : Ibu Dyna Afriyanti,Am.Far (082177434105)2.
92
+ Lab Biomedik : Ibu Peggy Dwi Pratiwi, Am.Ak (082281159219)3. Lab Anatomi : Bapak
93
+ Yusiro (085269413988)# Perpustakaan1. Lab Perpustakaan : Ibu Fristi (082182211964)#
94
+ Lab ICT-CBT1. Pendaftaran UKMPPD (Profesi Dokter), Pendaftaran UKNI (Profesi Ners),
95
+ Pendaftaran Yudisium, Penurunan UKT : Bapak Martinsyah, S.E (085266892842)2. Operator
96
+ Teknis ICT-CBT : Bapak Mory Setiawan, S.Pd (085268330478) Untuk pelayanan mahasiswa
97
+ yang berkaitan dengan akademik dapat menghubungi staf kependidikan masing-masing
98
+ jurusan pada program studi, atau bisa menghubungi kontak person sebagai berikut:
99
+ # Jurusan Pendidikan Dokter 1.  Program Studi Pendidikan Dokter : Ibu Melly Priyana,
100
+ SE (085208500620) Ruang Kerja : Ruang Akademik FKIK Unja 2. Program Studi Profesi
101
+ Dokter : Ibu Yuricho,S.Kep (085269962998) Ruang Kerja :Ruang Dekan FKIK Unja #
102
+ Jurusan Ilmu Keperawatan 1. Program Studi  Ilmu Keperawatan : Ibu Nursamsiah (082178444274)
103
+ Ruang kerja : Ruang Akademik FKIK Unja 2. Program Studi Profesi Ners : Ibu Nurlaila,S.Pd
104
+ (082177180778)Ruang kerja : Ruang Akademik FKIK Unja # Jurusan Ilmu Kesehatan
105
+ Masyarakat 1. Program Studi Ilmu Kesehatan Masyarakat : Bapak Yudhi Desfratama
106
+ Putra Alda, S.H. (085839603772) Ruang kerja : Ruang Program Studi Ilmu Kesehatan
107
+ Masyarakat # Jurusan Ilmu Psikolgi 1. Program Studi Psikologi : Ibu Lina (085383337698)
108
+ Ruang kerja : Ruang Dekan FKIK Unja # Jurusan Farmasi 1. Program Studi Farmasi
109
+ : Bapak Ides Nawinata, S.E (085269826454) Ruang kerja : Ruang Perlengkapan FKIK
110
+ Unja # Kemahasiswaan (Aktif Kuliah) 1. Kemahasiswaan : Bapak Irwansyah, S.K.M
111
+ (0895620070754) Ruang kerja : Ruang Perlengkapan FKIK Unja # Laboratorium 1. Lab
112
+ Keterampilan Klinik (SKillab) : Ibu Dyna Afriyanti,Am.Far (082177434105) 2. Lab
113
+ Biomedik : Ibu Peggy Dwi Pratiwi, Am.Ak (082281159219) 3. Lab Anatomi : Bapak
114
+ Yusiro (085269413988) # Perpustakaan 1. Lab Perpustakaan : Ibu Fristi (082182211964)
115
+ # Lab ICT-CBT 1. Pendaftaran UKMPPD (Profesi Dokter), Pendaftaran UKNI (Profesi
116
+ Ners), Pendaftaran Yudisium, Penurunan UKT : Bapak Martinsyah, S.E (085266892842)
117
+ 2. Operator Teknis ICT-CBT : Bapak Mory Setiawan, S.Pd (085268330478)'
118
+ - silakan reset passwordnya melalui link https://siharka.menpan.go.id/ pilih bagian
119
+ lupa password silakan reset passwordnya melalui link https://siharka.menpan.go.id/
120
+ pilih bagian lupa password
121
+ - source_sentence: 5. Saya Cek Ijazah Saya di Sivil Dikti, Ternyata Data Ijazah Saya
122
+ Tidak Terdaftar. Bagaimana Prosedur Selanjutnya?
123
+ sentences:
124
+ - Cari data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM
125
+ pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk
126
+ BAK UNJA melalaui WhatsApp di 0821 8147 6636 Cari data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan
127
+ menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan
128
+ hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636
129
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
130
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
131
+ 16 sks.
132
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
133
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
134
+ 16 sks.
135
+ pipeline_tag: sentence-similarity
136
+ library_name: sentence-transformers
137
+ ---
138
+
139
+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
140
+
141
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
142
+
143
+ ## Model Details
144
+
145
+ ### Model Description
146
+ - **Model Type:** Sentence Transformer
147
+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
148
+ - **Maximum Sequence Length:** 256 tokens
149
+ - **Output Dimensionality:** 384 dimensions
150
+ - **Similarity Function:** Cosine Similarity
151
+ <!-- - **Training Dataset:** Unknown -->
152
+ <!-- - **Language:** Unknown -->
153
+ <!-- - **License:** Unknown -->
154
+
155
+ ### Model Sources
156
+
157
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
158
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
159
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
160
+
161
+ ### Full Model Architecture
162
+
163
+ ```
164
+ SentenceTransformer(
165
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
166
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
167
+ (2): Normalize()
168
+ )
169
+ ```
170
+
171
+ ## Usage
172
+
173
+ ### Direct Usage (Sentence Transformers)
174
+
175
+ First install the Sentence Transformers library:
176
+
177
+ ```bash
178
+ pip install -U sentence-transformers
179
+ ```
180
+
181
+ Then you can load this model and run inference.
182
+ ```python
183
+ from sentence_transformers import SentenceTransformer
184
+
185
+ # Download from the 🤗 Hub
186
+ model = SentenceTransformer("sentence_transformers_model_id")
187
+ # Run inference
188
+ sentences = [
189
+ '5. Saya Cek Ijazah Saya di Sivil Dikti, Ternyata Data Ijazah Saya Tidak Terdaftar. Bagaimana Prosedur Selanjutnya?',
190
+ 'Cari\xa0data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636 Cari\xa0data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636',
191
+ 'Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16 sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16 sks.',
192
+ ]
193
+ embeddings = model.encode(sentences)
194
+ print(embeddings.shape)
195
+ # [3, 384]
196
+
197
+ # Get the similarity scores for the embeddings
198
+ similarities = model.similarity(embeddings, embeddings)
199
+ print(similarities.shape)
200
+ # [3, 3]
201
+ ```
202
+
203
+ <!--
204
+ ### Direct Usage (Transformers)
205
+
206
+ <details><summary>Click to see the direct usage in Transformers</summary>
207
+
208
+ </details>
209
+ -->
210
+
211
+ <!--
212
+ ### Downstream Usage (Sentence Transformers)
213
+
214
+ You can finetune this model on your own dataset.
215
+
216
+ <details><summary>Click to expand</summary>
217
+
218
+ </details>
219
+ -->
220
+
221
+ <!--
222
+ ### Out-of-Scope Use
223
+
224
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
225
+ -->
226
+
227
+ <!--
228
+ ## Bias, Risks and Limitations
229
+
230
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
231
+ -->
232
+
233
+ <!--
234
+ ### Recommendations
235
+
236
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
237
+ -->
238
+
239
+ ## Training Details
240
+
241
+ ### Training Dataset
242
+
243
+ #### Unnamed Dataset
244
+
245
+ * Size: 200 training samples
246
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
247
+ * Approximate statistics based on the first 200 samples:
248
+ | | sentence_0 | sentence_1 | sentence_2 |
249
+ |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
250
+ | type | string | string | string |
251
+ | details | <ul><li>min: 10 tokens</li><li>mean: 32.59 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 137.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 130.25 tokens</li><li>max: 256 tokens</li></ul> |
252
+ * Samples:
253
+ | sentence_0 | sentence_1 | sentence_2 |
254
+ |:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
255
+ | <code>Saya sudah mengajukan BKD kepada asesor, dan karena satu alasan saya ingin membatalkan ajuan BKD tersebut. Apa yang harus saya lakukan (Remunerasi)?</code> | <code>Klik tombol merahBuat Permohonan Pembatalan Ajuandi menuBKD dan Kinerja. Klik tombol merahBuat Permohonan Pembatalan Ajuandi menuBKD dan Kinerja.</code> | <code>Membuat surat permohonan yang ditujukan ke Dekan Fakultas PertanianSurat Keterangan Bebas Pustaka Fakultas PertanianSurat Keterangan Bebas Pustaka Universitas JambiBukti Registrasi Terakhir</code> |
256
+ | <code>Bagaimana cara mengakses atau login ke aplikasi SIREMUN (Remunerasi)?</code> | <code>Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama dengan akun akses ke SIAKAD. Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama dengan akun akses ke SIAKAD.</code> | <code>DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA.JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG) DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA. JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG)</code> |
257
+ | <code>Untuk menambahkan lokasi presensi Dosen (karena tugas tambahan), Siapa yang bisa dihubungi?</code> | <code>Silakan hubungi bagian kepegawaian BUPK Universitas Jambi Silakan hubungi bagian kepegawaian BUPK Universitas Jambi</code> | <code>DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA.JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG) DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA. JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG)</code> |
258
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
259
+ ```json
260
+ {
261
+ "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
262
+ "triplet_margin": 5
263
+ }
264
+ ```
265
+
266
+ ### Training Hyperparameters
267
+ #### Non-Default Hyperparameters
268
+
269
+ - `per_device_train_batch_size`: 16
270
+ - `per_device_eval_batch_size`: 16
271
+ - `multi_dataset_batch_sampler`: round_robin
272
+
273
+ #### All Hyperparameters
274
+ <details><summary>Click to expand</summary>
275
+
276
+ - `overwrite_output_dir`: False
277
+ - `do_predict`: False
278
+ - `eval_strategy`: no
279
+ - `prediction_loss_only`: True
280
+ - `per_device_train_batch_size`: 16
281
+ - `per_device_eval_batch_size`: 16
282
+ - `per_gpu_train_batch_size`: None
283
+ - `per_gpu_eval_batch_size`: None
284
+ - `gradient_accumulation_steps`: 1
285
+ - `eval_accumulation_steps`: None
286
+ - `torch_empty_cache_steps`: None
287
+ - `learning_rate`: 5e-05
288
+ - `weight_decay`: 0.0
289
+ - `adam_beta1`: 0.9
290
+ - `adam_beta2`: 0.999
291
+ - `adam_epsilon`: 1e-08
292
+ - `max_grad_norm`: 1
293
+ - `num_train_epochs`: 3
294
+ - `max_steps`: -1
295
+ - `lr_scheduler_type`: linear
296
+ - `lr_scheduler_kwargs`: {}
297
+ - `warmup_ratio`: 0.0
298
+ - `warmup_steps`: 0
299
+ - `log_level`: passive
300
+ - `log_level_replica`: warning
301
+ - `log_on_each_node`: True
302
+ - `logging_nan_inf_filter`: True
303
+ - `save_safetensors`: True
304
+ - `save_on_each_node`: False
305
+ - `save_only_model`: False
306
+ - `restore_callback_states_from_checkpoint`: False
307
+ - `no_cuda`: False
308
+ - `use_cpu`: False
309
+ - `use_mps_device`: False
310
+ - `seed`: 42
311
+ - `data_seed`: None
312
+ - `jit_mode_eval`: False
313
+ - `use_ipex`: False
314
+ - `bf16`: False
315
+ - `fp16`: False
316
+ - `fp16_opt_level`: O1
317
+ - `half_precision_backend`: auto
318
+ - `bf16_full_eval`: False
319
+ - `fp16_full_eval`: False
320
+ - `tf32`: None
321
+ - `local_rank`: 0
322
+ - `ddp_backend`: None
323
+ - `tpu_num_cores`: None
324
+ - `tpu_metrics_debug`: False
325
+ - `debug`: []
326
+ - `dataloader_drop_last`: False
327
+ - `dataloader_num_workers`: 0
328
+ - `dataloader_prefetch_factor`: None
329
+ - `past_index`: -1
330
+ - `disable_tqdm`: False
331
+ - `remove_unused_columns`: True
332
+ - `label_names`: None
333
+ - `load_best_model_at_end`: False
334
+ - `ignore_data_skip`: False
335
+ - `fsdp`: []
336
+ - `fsdp_min_num_params`: 0
337
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
338
+ - `fsdp_transformer_layer_cls_to_wrap`: None
339
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
340
+ - `deepspeed`: None
341
+ - `label_smoothing_factor`: 0.0
342
+ - `optim`: adamw_torch
343
+ - `optim_args`: None
344
+ - `adafactor`: False
345
+ - `group_by_length`: False
346
+ - `length_column_name`: length
347
+ - `ddp_find_unused_parameters`: None
348
+ - `ddp_bucket_cap_mb`: None
349
+ - `ddp_broadcast_buffers`: False
350
+ - `dataloader_pin_memory`: True
351
+ - `dataloader_persistent_workers`: False
352
+ - `skip_memory_metrics`: True
353
+ - `use_legacy_prediction_loop`: False
354
+ - `push_to_hub`: False
355
+ - `resume_from_checkpoint`: None
356
+ - `hub_model_id`: None
357
+ - `hub_strategy`: every_save
358
+ - `hub_private_repo`: None
359
+ - `hub_always_push`: False
360
+ - `gradient_checkpointing`: False
361
+ - `gradient_checkpointing_kwargs`: None
362
+ - `include_inputs_for_metrics`: False
363
+ - `include_for_metrics`: []
364
+ - `eval_do_concat_batches`: True
365
+ - `fp16_backend`: auto
366
+ - `push_to_hub_model_id`: None
367
+ - `push_to_hub_organization`: None
368
+ - `mp_parameters`:
369
+ - `auto_find_batch_size`: False
370
+ - `full_determinism`: False
371
+ - `torchdynamo`: None
372
+ - `ray_scope`: last
373
+ - `ddp_timeout`: 1800
374
+ - `torch_compile`: False
375
+ - `torch_compile_backend`: None
376
+ - `torch_compile_mode`: None
377
+ - `include_tokens_per_second`: False
378
+ - `include_num_input_tokens_seen`: False
379
+ - `neftune_noise_alpha`: None
380
+ - `optim_target_modules`: None
381
+ - `batch_eval_metrics`: False
382
+ - `eval_on_start`: False
383
+ - `use_liger_kernel`: False
384
+ - `eval_use_gather_object`: False
385
+ - `average_tokens_across_devices`: False
386
+ - `prompts`: None
387
+ - `batch_sampler`: batch_sampler
388
+ - `multi_dataset_batch_sampler`: round_robin
389
+
390
+ </details>
391
+
392
+ ### Framework Versions
393
+ - Python: 3.11.13
394
+ - Sentence Transformers: 4.1.0
395
+ - Transformers: 4.52.4
396
+ - PyTorch: 2.6.0+cu124
397
+ - Accelerate: 1.7.0
398
+ - Datasets: 2.14.4
399
+ - Tokenizers: 0.21.1
400
+
401
+ ## Citation
402
+
403
+ ### BibTeX
404
+
405
+ #### Sentence Transformers
406
+ ```bibtex
407
+ @inproceedings{reimers-2019-sentence-bert,
408
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
409
+ author = "Reimers, Nils and Gurevych, Iryna",
410
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
411
+ month = "11",
412
+ year = "2019",
413
+ publisher = "Association for Computational Linguistics",
414
+ url = "https://arxiv.org/abs/1908.10084",
415
+ }
416
+ ```
417
+
418
+ #### TripletLoss
419
+ ```bibtex
420
+ @misc{hermans2017defense,
421
+ title={In Defense of the Triplet Loss for Person Re-Identification},
422
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
423
+ year={2017},
424
+ eprint={1703.07737},
425
+ archivePrefix={arXiv},
426
+ primaryClass={cs.CV}
427
+ }
428
+ ```
429
+
430
+ <!--
431
+ ## Glossary
432
+
433
+ *Clearly define terms in order to be accessible across audiences.*
434
+ -->
435
+
436
+ <!--
437
+ ## Model Card Authors
438
+
439
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
440
+ -->
441
+
442
+ <!--
443
+ ## Model Card Contact
444
+
445
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
446
+ -->
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rahul-sbert-model/README.md ADDED
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1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:200
8
+ - loss:TripletLoss
9
+ base_model: sentence-transformers/all-MiniLM-L6-v2
10
+ widget:
11
+ - source_sentence: '[ELISTA] Apa saja kegiatan utama yang dikelola oleh ELISTA? (Tugas
12
+ Akhir/Skripsi/Tesis/Disertasi)?'
13
+ sentences:
14
+ - Pengajuan judul tugas akhir.Pembuatan alur waktu (timeline) tugas akhir.Pembimbingan
15
+ secara online dan offline.Pembuatan undangan dan jadwal seminar/sidang tugas akhir.Manajemen
16
+ tugas akhir untuk pengelola tugas akhir.Informasi terkait mahasiswa bimbingan,
17
+ serta rekap dosen pembimbing, dosen penguji, dan mahasiswa bimbingan.Berbagai
18
+ informasi bagi dosen seperti jadwal konsultasi, jadwal seminar/sidang dan lainnya.Berbagai
19
+ informasi bagi mahasiswa seperti penerimaan/penolakan ajuan judul tugas akhir,
20
+ respon bimbingan oleh dosen, dan lainnya. Pengajuan judul tugas akhir. Pembuatan
21
+ alur waktu (timeline) tugas akhir. Pembimbingan secara online dan offline. Pembuatan
22
+ undangan dan jadwal seminar/sidang tugas akhir. Manajemen tugas akhir untuk pengelola
23
+ tugas akhir. Informasi terkait mahasiswa bimbingan, serta rekap dosen pembimbing,
24
+ dosen penguji, dan mahasiswa bimbingan. Berbagai informasi bagi dosen seperti
25
+ jadwal konsultasi, jadwal seminar/sidang dan lainnya. Berbagai informasi bagi
26
+ mahasiswa seperti penerimaan/penolakan ajuan judul tugas akhir, respon bimbingan
27
+ oleh dosen, dan lainnya.
28
+ - 'Jumlah Poin maksimal kelebihan kinerja yang dapat dibayarkan:Untuk Dosen biasa
29
+ = 56 poin (150%)Untuk Dosen dengan tugas tambahan = 42 poin (150%) Jumlah Poin
30
+ maksimal kelebihan kinerja yang dapat dibayarkan: Untuk Dosen biasa = 56 poin
31
+ (150%) Untuk Dosen dengan tugas tambahan = 42 poin (150%)'
32
+ - FOTOCOPY KONVERSI NIPFOTOCOPY SK CPNSFOTOCOPY SK PNSFOTOCOPY SK PANGKAT TERAKHIRDAFTAR
33
+ KELUARGA PNS (DIKETAHUI ATASAN LANGSUNG)LAPORAN PERKAWINAN PERTAMA (DIKETAHUI
34
+ ATASAN LANGSUNG)FOTOCOPY SURAT NIKAH YANG DI LEGALISIR OLEH ATASAN LANGSUNGPAS
35
+ FOTO UKURAN 3X4 (HITAM PUTIH) 3 LEMBAR (SUAMI+ISTRI)LAPORAN PERKAWINAN KEDUA (DIKETAHUI
36
+ ATASAN LANGSUNG)*
37
+ - source_sentence: Bagaimana cara mendapatkan Surat Keterangan Sedang Tidak Menerima
38
+ Beasiswa?
39
+ sentences:
40
+ - Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan
41
+ menggunakan akun yang sama dengan akun akses ke SIAKAD. Anda dapat mengakses atau
42
+ login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama
43
+ dengan akun akses ke SIAKAD.
44
+ - 1. Menemui Wakil Dekan III Bidang Kemahasiswaan dan Alumni Keguruan dan Ilmu Pendidikanguna
45
+ mendapatkan Memomengenai hal terkait2. Membawa memo tersebut pada bagian Kemahasiswaan
46
+ FKIP 1. Menemui Wakil Dekan III Bidang Kemahasiswaan dan Alumni Keguruan dan Ilmu
47
+ Pendidikanguna mendapatkan Memomengenai hal terkait 2. Membawa memo tersebut pada
48
+ bagian Kemahasiswaan FKIP
49
+ - Tidak boleh dihapus, tapi ajukan permohonan perbaikan/penyesuaian MK pada kurikulum
50
+ melalui Dekan/Wakil Dekan Akademik kemudian disampaikan kepada Wakil Rektor Bidang
51
+ Akademik. Tidak boleh dihapus, tapi ajukan permohonan perbaikan/penyesuaian MK
52
+ pada kurikulum melalui Dekan/Wakil Dekan Akademik kemudian disampaikan kepada
53
+ Wakil Rektor Bidang Akademik.
54
+ - source_sentence: bagaimana prosedur pembuatan surat keterangan lulus (SKL)?
55
+ sentences:
56
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
57
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
58
+ 16 sks.
59
+ - Surat Keterangan Lulus (SKL) dapat diajukan apabila mahasiswa telah mendaftar
60
+ yudisium fakultas.1. Mahasiswa mengajukan surat permohonan yang ditujukan ke Dekan
61
+ Fakultas Sains dan Teknologi2. Melampirkan berita acara ujian skripsi dan transkrip
62
+ nilai sementara Surat Keterangan Lulus (SKL) dapat diajukan apabila mahasiswa
63
+ telah mendaftar yudisium fakultas. 1. Mahasiswa mengajukan surat permohonan yang
64
+ ditujukan ke Dekan Fakultas Sains dan Teknologi 2. Melampirkan berita acara ujian
65
+ skripsi dan transkrip nilai sementara
66
+ - Password simpeg sama dengan password siakad,bila password siakad pun lupa silahkan
67
+ datang ke helpdesk LPTIK atau kirim email ke [email protected] Password
68
+ simpeg sama dengan password siakad, bila password siakad pun lupa silahkan datang
69
+ ke helpdesk LPTIK atau kirim email ke [email protected]
70
+ - source_sentence: Jika asesor pertama menyatakan valid, tapi asesor kedua menyatakan
71
+ tidak valid terhadap BKD saya, apa yang harus saya lakukan (Remunerasi)?
72
+ sentences:
73
+ - Perhatikan saran/komentar dari asesor kedua dan kemudian tindaklanjuti sesuai
74
+ saran/komentar dari asesor kedua. Perhatikan saran/komentar dari asesor kedua
75
+ dan kemudian tindaklanjuti sesuai saran/komentar dari asesor kedua.
76
+ - 'Untuk pelayanan mahasiswa yang berkaitan dengan akademik dapat menghubungi staf
77
+ kependidikan masing-masing jurusan pada program studi, atau bisa menghubungi kontak
78
+ person sebagai berikut:# Jurusan Pendidikan Dokter1.  Program Studi Pendidikan
79
+ Dokter : Ibu Melly Priyana, SE (085208500620) Ruang Kerja : Ruang Akademik FKIK
80
+ Unja2. Program Studi Profesi Dokter : Ibu Yuricho,S.Kep (085269962998) Ruang Kerja
81
+ :Ruang Dekan FKIK Unja# Jurusan Ilmu Keperawatan1. Program Studi  Ilmu Keperawatan
82
+ : Ibu Nursamsiah (082178444274) Ruang kerja : Ruang Akademik FKIK Unja2. Program
83
+ Studi Profesi Ners : Ibu Nurlaila,S.Pd (082177180778)Ruang kerja : Ruang Akademik
84
+ FKIK Unja# Jurusan Ilmu Kesehatan Masyarakat1. Program Studi Ilmu Kesehatan Masyarakat
85
+ : Bapak Yudhi Desfratama Putra Alda, S.H. (085839603772) Ruang kerja : Ruang Program
86
+ Studi Ilmu Kesehatan Masyarakat# Jurusan Ilmu Psikolgi1. Program Studi Psikologi
87
+ : Ibu Lina (085383337698) Ruang kerja : Ruang Dekan FKIK Unja# Jurusan Farmasi1.
88
+ Program Studi Farmasi : Bapak Ides Nawinata, S.E (085269826454) Ruang kerja :
89
+ Ruang Perlengkapan FKIK Unja# Kemahasiswaan (Aktif Kuliah)1. Kemahasiswaan : Bapak
90
+ Irwansyah, S.K.M (0895620070754) Ruang kerja : Ruang Perlengkapan FKIK Unja# Laboratorium1.
91
+ Lab Keterampilan Klinik (SKillab) : Ibu Dyna Afriyanti,Am.Far (082177434105)2.
92
+ Lab Biomedik : Ibu Peggy Dwi Pratiwi, Am.Ak (082281159219)3. Lab Anatomi : Bapak
93
+ Yusiro (085269413988)# Perpustakaan1. Lab Perpustakaan : Ibu Fristi (082182211964)#
94
+ Lab ICT-CBT1. Pendaftaran UKMPPD (Profesi Dokter), Pendaftaran UKNI (Profesi Ners),
95
+ Pendaftaran Yudisium, Penurunan UKT : Bapak Martinsyah, S.E (085266892842)2. Operator
96
+ Teknis ICT-CBT : Bapak Mory Setiawan, S.Pd (085268330478) Untuk pelayanan mahasiswa
97
+ yang berkaitan dengan akademik dapat menghubungi staf kependidikan masing-masing
98
+ jurusan pada program studi, atau bisa menghubungi kontak person sebagai berikut:
99
+ # Jurusan Pendidikan Dokter 1.  Program Studi Pendidikan Dokter : Ibu Melly Priyana,
100
+ SE (085208500620) Ruang Kerja : Ruang Akademik FKIK Unja 2. Program Studi Profesi
101
+ Dokter : Ibu Yuricho,S.Kep (085269962998) Ruang Kerja :Ruang Dekan FKIK Unja #
102
+ Jurusan Ilmu Keperawatan 1. Program Studi  Ilmu Keperawatan : Ibu Nursamsiah (082178444274)
103
+ Ruang kerja : Ruang Akademik FKIK Unja 2. Program Studi Profesi Ners : Ibu Nurlaila,S.Pd
104
+ (082177180778)Ruang kerja : Ruang Akademik FKIK Unja # Jurusan Ilmu Kesehatan
105
+ Masyarakat 1. Program Studi Ilmu Kesehatan Masyarakat : Bapak Yudhi Desfratama
106
+ Putra Alda, S.H. (085839603772) Ruang kerja : Ruang Program Studi Ilmu Kesehatan
107
+ Masyarakat # Jurusan Ilmu Psikolgi 1. Program Studi Psikologi : Ibu Lina (085383337698)
108
+ Ruang kerja : Ruang Dekan FKIK Unja # Jurusan Farmasi 1. Program Studi Farmasi
109
+ : Bapak Ides Nawinata, S.E (085269826454) Ruang kerja : Ruang Perlengkapan FKIK
110
+ Unja # Kemahasiswaan (Aktif Kuliah) 1. Kemahasiswaan : Bapak Irwansyah, S.K.M
111
+ (0895620070754) Ruang kerja : Ruang Perlengkapan FKIK Unja # Laboratorium 1. Lab
112
+ Keterampilan Klinik (SKillab) : Ibu Dyna Afriyanti,Am.Far (082177434105) 2. Lab
113
+ Biomedik : Ibu Peggy Dwi Pratiwi, Am.Ak (082281159219) 3. Lab Anatomi : Bapak
114
+ Yusiro (085269413988) # Perpustakaan 1. Lab Perpustakaan : Ibu Fristi (082182211964)
115
+ # Lab ICT-CBT 1. Pendaftaran UKMPPD (Profesi Dokter), Pendaftaran UKNI (Profesi
116
+ Ners), Pendaftaran Yudisium, Penurunan UKT : Bapak Martinsyah, S.E (085266892842)
117
+ 2. Operator Teknis ICT-CBT : Bapak Mory Setiawan, S.Pd (085268330478)'
118
+ - silakan reset passwordnya melalui link https://siharka.menpan.go.id/ pilih bagian
119
+ lupa password silakan reset passwordnya melalui link https://siharka.menpan.go.id/
120
+ pilih bagian lupa password
121
+ - source_sentence: 5. Saya Cek Ijazah Saya di Sivil Dikti, Ternyata Data Ijazah Saya
122
+ Tidak Terdaftar. Bagaimana Prosedur Selanjutnya?
123
+ sentences:
124
+ - Cari data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM
125
+ pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk
126
+ BAK UNJA melalaui WhatsApp di 0821 8147 6636 Cari data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan
127
+ menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan
128
+ hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636
129
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
130
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
131
+ 16 sks.
132
+ - Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16
133
+ sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari
134
+ 16 sks.
135
+ pipeline_tag: sentence-similarity
136
+ library_name: sentence-transformers
137
+ ---
138
+
139
+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
140
+
141
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
142
+
143
+ ## Model Details
144
+
145
+ ### Model Description
146
+ - **Model Type:** Sentence Transformer
147
+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
148
+ - **Maximum Sequence Length:** 256 tokens
149
+ - **Output Dimensionality:** 384 dimensions
150
+ - **Similarity Function:** Cosine Similarity
151
+ <!-- - **Training Dataset:** Unknown -->
152
+ <!-- - **Language:** Unknown -->
153
+ <!-- - **License:** Unknown -->
154
+
155
+ ### Model Sources
156
+
157
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
158
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
159
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
160
+
161
+ ### Full Model Architecture
162
+
163
+ ```
164
+ SentenceTransformer(
165
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
166
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
167
+ (2): Normalize()
168
+ )
169
+ ```
170
+
171
+ ## Usage
172
+
173
+ ### Direct Usage (Sentence Transformers)
174
+
175
+ First install the Sentence Transformers library:
176
+
177
+ ```bash
178
+ pip install -U sentence-transformers
179
+ ```
180
+
181
+ Then you can load this model and run inference.
182
+ ```python
183
+ from sentence_transformers import SentenceTransformer
184
+
185
+ # Download from the 🤗 Hub
186
+ model = SentenceTransformer("sentence_transformers_model_id")
187
+ # Run inference
188
+ sentences = [
189
+ '5. Saya Cek Ijazah Saya di Sivil Dikti, Ternyata Data Ijazah Saya Tidak Terdaftar. Bagaimana Prosedur Selanjutnya?',
190
+ 'Cari\xa0data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636 Cari\xa0data anda pada linkhttps://pddikti.kemdikbud.go.id/dengan menginputkan NIM pada kolom pencarian.Bila data tetap tidak ditemukan silahkan hubungi Helpdesk BAK UNJA melalaui WhatsApp di 0821 8147 6636',
191
+ 'Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16 sks. Kelebihan kinerja dapat dibayarkan jika BKD terpenuhi dan kinerja lebih dari 16 sks.',
192
+ ]
193
+ embeddings = model.encode(sentences)
194
+ print(embeddings.shape)
195
+ # [3, 384]
196
+
197
+ # Get the similarity scores for the embeddings
198
+ similarities = model.similarity(embeddings, embeddings)
199
+ print(similarities.shape)
200
+ # [3, 3]
201
+ ```
202
+
203
+ <!--
204
+ ### Direct Usage (Transformers)
205
+
206
+ <details><summary>Click to see the direct usage in Transformers</summary>
207
+
208
+ </details>
209
+ -->
210
+
211
+ <!--
212
+ ### Downstream Usage (Sentence Transformers)
213
+
214
+ You can finetune this model on your own dataset.
215
+
216
+ <details><summary>Click to expand</summary>
217
+
218
+ </details>
219
+ -->
220
+
221
+ <!--
222
+ ### Out-of-Scope Use
223
+
224
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
225
+ -->
226
+
227
+ <!--
228
+ ## Bias, Risks and Limitations
229
+
230
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
231
+ -->
232
+
233
+ <!--
234
+ ### Recommendations
235
+
236
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
237
+ -->
238
+
239
+ ## Training Details
240
+
241
+ ### Training Dataset
242
+
243
+ #### Unnamed Dataset
244
+
245
+ * Size: 200 training samples
246
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
247
+ * Approximate statistics based on the first 200 samples:
248
+ | | sentence_0 | sentence_1 | sentence_2 |
249
+ |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
250
+ | type | string | string | string |
251
+ | details | <ul><li>min: 10 tokens</li><li>mean: 32.59 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 137.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 130.25 tokens</li><li>max: 256 tokens</li></ul> |
252
+ * Samples:
253
+ | sentence_0 | sentence_1 | sentence_2 |
254
+ |:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
255
+ | <code>Saya sudah mengajukan BKD kepada asesor, dan karena satu alasan saya ingin membatalkan ajuan BKD tersebut. Apa yang harus saya lakukan (Remunerasi)?</code> | <code>Klik tombol merahBuat Permohonan Pembatalan Ajuandi menuBKD dan Kinerja. Klik tombol merahBuat Permohonan Pembatalan Ajuandi menuBKD dan Kinerja.</code> | <code>Membuat surat permohonan yang ditujukan ke Dekan Fakultas PertanianSurat Keterangan Bebas Pustaka Fakultas PertanianSurat Keterangan Bebas Pustaka Universitas JambiBukti Registrasi Terakhir</code> |
256
+ | <code>Bagaimana cara mengakses atau login ke aplikasi SIREMUN (Remunerasi)?</code> | <code>Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama dengan akun akses ke SIAKAD. Anda dapat mengakses atau login ke dalam aplikasi SIREMUN (Remunerasi) dengan menggunakan akun yang sama dengan akun akses ke SIAKAD.</code> | <code>DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA.JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG) DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA. JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG)</code> |
257
+ | <code>Untuk menambahkan lokasi presensi Dosen (karena tugas tambahan), Siapa yang bisa dihubungi?</code> | <code>Silakan hubungi bagian kepegawaian BUPK Universitas Jambi Silakan hubungi bagian kepegawaian BUPK Universitas Jambi</code> | <code>DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA.JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG) DATA IPK ALUMNI UNJA TELAH KAMI LAPORKAN KE PANGKALAN DATA DIKTI (PDDIKTI) SESUSAI DENGAN NILAI IPK YANG TERTERA PADA TRANSKRIP NILAI ANDA. JIKA SETELAH PELAPORAN DATA INI MASIH DITEMUKAN KETIDAKSESUAIAN IPK ANTARA TRANSKRIP NILAI UNJA DENGAN APLIKASI PPG SILAHKAN ANDA MENGKONFIRMASI KE ADMIN APILIKASI PPG (CEK DI WEBSITE PPG)</code> |
258
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
259
+ ```json
260
+ {
261
+ "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
262
+ "triplet_margin": 5
263
+ }
264
+ ```
265
+
266
+ ### Training Hyperparameters
267
+ #### Non-Default Hyperparameters
268
+
269
+ - `per_device_train_batch_size`: 16
270
+ - `per_device_eval_batch_size`: 16
271
+ - `multi_dataset_batch_sampler`: round_robin
272
+
273
+ #### All Hyperparameters
274
+ <details><summary>Click to expand</summary>
275
+
276
+ - `overwrite_output_dir`: False
277
+ - `do_predict`: False
278
+ - `eval_strategy`: no
279
+ - `prediction_loss_only`: True
280
+ - `per_device_train_batch_size`: 16
281
+ - `per_device_eval_batch_size`: 16
282
+ - `per_gpu_train_batch_size`: None
283
+ - `per_gpu_eval_batch_size`: None
284
+ - `gradient_accumulation_steps`: 1
285
+ - `eval_accumulation_steps`: None
286
+ - `torch_empty_cache_steps`: None
287
+ - `learning_rate`: 5e-05
288
+ - `weight_decay`: 0.0
289
+ - `adam_beta1`: 0.9
290
+ - `adam_beta2`: 0.999
291
+ - `adam_epsilon`: 1e-08
292
+ - `max_grad_norm`: 1
293
+ - `num_train_epochs`: 3
294
+ - `max_steps`: -1
295
+ - `lr_scheduler_type`: linear
296
+ - `lr_scheduler_kwargs`: {}
297
+ - `warmup_ratio`: 0.0
298
+ - `warmup_steps`: 0
299
+ - `log_level`: passive
300
+ - `log_level_replica`: warning
301
+ - `log_on_each_node`: True
302
+ - `logging_nan_inf_filter`: True
303
+ - `save_safetensors`: True
304
+ - `save_on_each_node`: False
305
+ - `save_only_model`: False
306
+ - `restore_callback_states_from_checkpoint`: False
307
+ - `no_cuda`: False
308
+ - `use_cpu`: False
309
+ - `use_mps_device`: False
310
+ - `seed`: 42
311
+ - `data_seed`: None
312
+ - `jit_mode_eval`: False
313
+ - `use_ipex`: False
314
+ - `bf16`: False
315
+ - `fp16`: False
316
+ - `fp16_opt_level`: O1
317
+ - `half_precision_backend`: auto
318
+ - `bf16_full_eval`: False
319
+ - `fp16_full_eval`: False
320
+ - `tf32`: None
321
+ - `local_rank`: 0
322
+ - `ddp_backend`: None
323
+ - `tpu_num_cores`: None
324
+ - `tpu_metrics_debug`: False
325
+ - `debug`: []
326
+ - `dataloader_drop_last`: False
327
+ - `dataloader_num_workers`: 0
328
+ - `dataloader_prefetch_factor`: None
329
+ - `past_index`: -1
330
+ - `disable_tqdm`: False
331
+ - `remove_unused_columns`: True
332
+ - `label_names`: None
333
+ - `load_best_model_at_end`: False
334
+ - `ignore_data_skip`: False
335
+ - `fsdp`: []
336
+ - `fsdp_min_num_params`: 0
337
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
338
+ - `fsdp_transformer_layer_cls_to_wrap`: None
339
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
340
+ - `deepspeed`: None
341
+ - `label_smoothing_factor`: 0.0
342
+ - `optim`: adamw_torch
343
+ - `optim_args`: None
344
+ - `adafactor`: False
345
+ - `group_by_length`: False
346
+ - `length_column_name`: length
347
+ - `ddp_find_unused_parameters`: None
348
+ - `ddp_bucket_cap_mb`: None
349
+ - `ddp_broadcast_buffers`: False
350
+ - `dataloader_pin_memory`: True
351
+ - `dataloader_persistent_workers`: False
352
+ - `skip_memory_metrics`: True
353
+ - `use_legacy_prediction_loop`: False
354
+ - `push_to_hub`: False
355
+ - `resume_from_checkpoint`: None
356
+ - `hub_model_id`: None
357
+ - `hub_strategy`: every_save
358
+ - `hub_private_repo`: None
359
+ - `hub_always_push`: False
360
+ - `gradient_checkpointing`: False
361
+ - `gradient_checkpointing_kwargs`: None
362
+ - `include_inputs_for_metrics`: False
363
+ - `include_for_metrics`: []
364
+ - `eval_do_concat_batches`: True
365
+ - `fp16_backend`: auto
366
+ - `push_to_hub_model_id`: None
367
+ - `push_to_hub_organization`: None
368
+ - `mp_parameters`:
369
+ - `auto_find_batch_size`: False
370
+ - `full_determinism`: False
371
+ - `torchdynamo`: None
372
+ - `ray_scope`: last
373
+ - `ddp_timeout`: 1800
374
+ - `torch_compile`: False
375
+ - `torch_compile_backend`: None
376
+ - `torch_compile_mode`: None
377
+ - `include_tokens_per_second`: False
378
+ - `include_num_input_tokens_seen`: False
379
+ - `neftune_noise_alpha`: None
380
+ - `optim_target_modules`: None
381
+ - `batch_eval_metrics`: False
382
+ - `eval_on_start`: False
383
+ - `use_liger_kernel`: False
384
+ - `eval_use_gather_object`: False
385
+ - `average_tokens_across_devices`: False
386
+ - `prompts`: None
387
+ - `batch_sampler`: batch_sampler
388
+ - `multi_dataset_batch_sampler`: round_robin
389
+
390
+ </details>
391
+
392
+ ### Framework Versions
393
+ - Python: 3.11.13
394
+ - Sentence Transformers: 4.1.0
395
+ - Transformers: 4.52.4
396
+ - PyTorch: 2.6.0+cu124
397
+ - Accelerate: 1.7.0
398
+ - Datasets: 2.14.4
399
+ - Tokenizers: 0.21.1
400
+
401
+ ## Citation
402
+
403
+ ### BibTeX
404
+
405
+ #### Sentence Transformers
406
+ ```bibtex
407
+ @inproceedings{reimers-2019-sentence-bert,
408
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
409
+ author = "Reimers, Nils and Gurevych, Iryna",
410
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
411
+ month = "11",
412
+ year = "2019",
413
+ publisher = "Association for Computational Linguistics",
414
+ url = "https://arxiv.org/abs/1908.10084",
415
+ }
416
+ ```
417
+
418
+ #### TripletLoss
419
+ ```bibtex
420
+ @misc{hermans2017defense,
421
+ title={In Defense of the Triplet Loss for Person Re-Identification},
422
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
423
+ year={2017},
424
+ eprint={1703.07737},
425
+ archivePrefix={arXiv},
426
+ primaryClass={cs.CV}
427
+ }
428
+ ```
429
+
430
+ <!--
431
+ ## Glossary
432
+
433
+ *Clearly define terms in order to be accessible across audiences.*
434
+ -->
435
+
436
+ <!--
437
+ ## Model Card Authors
438
+
439
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
440
+ -->
441
+
442
+ <!--
443
+ ## Model Card Contact
444
+
445
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
446
+ -->
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sample_data/README.md ADDED
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1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
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+ ]
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1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:1200
8
+ - loss:CosineSimilarityLoss
9
+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
10
+ widget:
11
+ - source_sentence: Sekelompok orang di perahu berlomba menuju pantai.
12
+ sentences:
13
+ - Haruskah kedatangan pada titik ini menjadi fokus untuk pemeriksaan sistem apa
14
+ pun?
15
+ - Sekelompok orang berdiri bersama dan berpose di depan sebuah rumah.
16
+ - Seperti yang tercakup dalam jawaban lain, satu-satunya pilihan Anda adalah menemui
17
+ dokter hewan untuk melakukan operasi.
18
+ - source_sentence: Industri Musik Memberi Penghormatan kepada Whitney Houston
19
+ sentences:
20
+ - Setiap bahasa diresapi oleh nilai-nilai budaya yang dibenamkannya.
21
+ - Sebuah pesawat merah dan putih terbang di hari yang cerah.
22
+ - Industri musik berduka atas Whitney Houston
23
+ - source_sentence: Pria itu menggunakan pedang untuk mengiris botol plastik.
24
+ sentences:
25
+ - Seorang pria sedang melakukan push up.
26
+ - Seorang pria mengiris botol plastik dengan pedang.
27
+ - Untuk melatih diri Anda menggunakan pintasan keyboard, cabut mouse Anda selama
28
+ beberapa hari.
29
+ - source_sentence: seorang anak laki-laki memanjat dinding batu
30
+ sentences:
31
+ - Anak memerah susu sapi di luar.
32
+ - Selalu sulit ketika orang tua menolak untuk melihat apa yang tampak jelas bagi
33
+ seorang guru.
34
+ - Lima bulldog sedang berlomba di jalur pasir.
35
+ - source_sentence: Pekerjaan pembongkaran dimulai di Stadion Don Valley
36
+ sentences:
37
+ - Kedua wanita itu sedang bermain game.
38
+ - Tingkat pemogokan bowling didefinisikan untuk pemain bowling sebagai jumlah rata-rata
39
+ bola yang dilempar per gawang yang diambil.
40
+ - Pekerjaan pembongkaran dimulai di stadion
41
+ pipeline_tag: sentence-similarity
42
+ library_name: sentence-transformers
43
+ metrics:
44
+ - pearson_cosine
45
+ - spearman_cosine
46
+ model-index:
47
+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
48
+ results:
49
+ - task:
50
+ type: semantic-similarity
51
+ name: Semantic Similarity
52
+ dataset:
53
+ name: sts eval
54
+ type: sts-eval
55
+ metrics:
56
+ - type: pearson_cosine
57
+ value: -0.04061959608314808
58
+ name: Pearson Cosine
59
+ - type: spearman_cosine
60
+ value: -0.07013283702354331
61
+ name: Spearman Cosine
62
+ ---
63
+
64
+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
65
+
66
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
67
+
68
+ ## Model Details
69
+
70
+ ### Model Description
71
+ - **Model Type:** Sentence Transformer
72
+ - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
73
+ - **Maximum Sequence Length:** 128 tokens
74
+ - **Output Dimensionality:** 384 dimensions
75
+ - **Similarity Function:** Cosine Similarity
76
+ <!-- - **Training Dataset:** Unknown -->
77
+ <!-- - **Language:** Unknown -->
78
+ <!-- - **License:** Unknown -->
79
+
80
+ ### Model Sources
81
+
82
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
83
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
84
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
85
+
86
+ ### Full Model Architecture
87
+
88
+ ```
89
+ SentenceTransformer(
90
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
91
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
92
+ )
93
+ ```
94
+
95
+ ## Usage
96
+
97
+ ### Direct Usage (Sentence Transformers)
98
+
99
+ First install the Sentence Transformers library:
100
+
101
+ ```bash
102
+ pip install -U sentence-transformers
103
+ ```
104
+
105
+ Then you can load this model and run inference.
106
+ ```python
107
+ from sentence_transformers import SentenceTransformer
108
+
109
+ # Download from the 🤗 Hub
110
+ model = SentenceTransformer("sentence_transformers_model_id")
111
+ # Run inference
112
+ sentences = [
113
+ 'Pekerjaan pembongkaran dimulai di Stadion Don Valley',
114
+ 'Pekerjaan pembongkaran dimulai di stadion',
115
+ 'Kedua wanita itu sedang bermain game.',
116
+ ]
117
+ embeddings = model.encode(sentences)
118
+ print(embeddings.shape)
119
+ # [3, 384]
120
+
121
+ # Get the similarity scores for the embeddings
122
+ similarities = model.similarity(embeddings, embeddings)
123
+ print(similarities.shape)
124
+ # [3, 3]
125
+ ```
126
+
127
+ <!--
128
+ ### Direct Usage (Transformers)
129
+
130
+ <details><summary>Click to see the direct usage in Transformers</summary>
131
+
132
+ </details>
133
+ -->
134
+
135
+ <!--
136
+ ### Downstream Usage (Sentence Transformers)
137
+
138
+ You can finetune this model on your own dataset.
139
+
140
+ <details><summary>Click to expand</summary>
141
+
142
+ </details>
143
+ -->
144
+
145
+ <!--
146
+ ### Out-of-Scope Use
147
+
148
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
149
+ -->
150
+
151
+ ## Evaluation
152
+
153
+ ### Metrics
154
+
155
+ #### Semantic Similarity
156
+
157
+ * Dataset: `sts-eval`
158
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
159
+
160
+ | Metric | Value |
161
+ |:--------------------|:------------|
162
+ | pearson_cosine | -0.0406 |
163
+ | **spearman_cosine** | **-0.0701** |
164
+
165
+ <!--
166
+ ## Bias, Risks and Limitations
167
+
168
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
169
+ -->
170
+
171
+ <!--
172
+ ### Recommendations
173
+
174
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
175
+ -->
176
+
177
+ ## Training Details
178
+
179
+ ### Training Dataset
180
+
181
+ #### Unnamed Dataset
182
+
183
+ * Size: 1,200 training samples
184
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
185
+ * Approximate statistics based on the first 1000 samples:
186
+ | | sentence_0 | sentence_1 | label |
187
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
188
+ | type | string | string | float |
189
+ | details | <ul><li>min: 5 tokens</li><li>mean: 16.78 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.67 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 115.43</li><li>max: 398.6</li></ul> |
190
+ * Samples:
191
+ | sentence_0 | sentence_1 | label |
192
+ |:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:-------------------|
193
+ | <code>Pendekatan terbaik adalah bermain dengan kucing Anda setiap malam sebelum tidur.</code> | <code>Trik yang saya lakukan adalah memegang dan memeluk kucing saya erat-erat jika mereka mengganggu saya di malam hari.</code> | <code>234.8</code> |
194
+ | <code>Anjing berwarna gelap berdiri di lapangan berumput.</code> | <code>Seekor anjing abu-abu berdiri di lapangan berumput.</code> | <code>133.6</code> |
195
+ | <code>Bebek putih berdiri di tanah.</code> | <code>Seorang wanita bertopi hitam berdiri di dekat kereta bawah tanah.</code> | <code>96.6</code> |
196
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
197
+ ```json
198
+ {
199
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
200
+ }
201
+ ```
202
+
203
+ ### Training Hyperparameters
204
+ #### Non-Default Hyperparameters
205
+
206
+ - `eval_strategy`: steps
207
+ - `per_device_train_batch_size`: 16
208
+ - `per_device_eval_batch_size`: 16
209
+ - `num_train_epochs`: 4
210
+ - `multi_dataset_batch_sampler`: round_robin
211
+
212
+ #### All Hyperparameters
213
+ <details><summary>Click to expand</summary>
214
+
215
+ - `overwrite_output_dir`: False
216
+ - `do_predict`: False
217
+ - `eval_strategy`: steps
218
+ - `prediction_loss_only`: True
219
+ - `per_device_train_batch_size`: 16
220
+ - `per_device_eval_batch_size`: 16
221
+ - `per_gpu_train_batch_size`: None
222
+ - `per_gpu_eval_batch_size`: None
223
+ - `gradient_accumulation_steps`: 1
224
+ - `eval_accumulation_steps`: None
225
+ - `torch_empty_cache_steps`: None
226
+ - `learning_rate`: 5e-05
227
+ - `weight_decay`: 0.0
228
+ - `adam_beta1`: 0.9
229
+ - `adam_beta2`: 0.999
230
+ - `adam_epsilon`: 1e-08
231
+ - `max_grad_norm`: 1
232
+ - `num_train_epochs`: 4
233
+ - `max_steps`: -1
234
+ - `lr_scheduler_type`: linear
235
+ - `lr_scheduler_kwargs`: {}
236
+ - `warmup_ratio`: 0.0
237
+ - `warmup_steps`: 0
238
+ - `log_level`: passive
239
+ - `log_level_replica`: warning
240
+ - `log_on_each_node`: True
241
+ - `logging_nan_inf_filter`: True
242
+ - `save_safetensors`: True
243
+ - `save_on_each_node`: False
244
+ - `save_only_model`: False
245
+ - `restore_callback_states_from_checkpoint`: False
246
+ - `no_cuda`: False
247
+ - `use_cpu`: False
248
+ - `use_mps_device`: False
249
+ - `seed`: 42
250
+ - `data_seed`: None
251
+ - `jit_mode_eval`: False
252
+ - `use_ipex`: False
253
+ - `bf16`: False
254
+ - `fp16`: False
255
+ - `fp16_opt_level`: O1
256
+ - `half_precision_backend`: auto
257
+ - `bf16_full_eval`: False
258
+ - `fp16_full_eval`: False
259
+ - `tf32`: None
260
+ - `local_rank`: 0
261
+ - `ddp_backend`: None
262
+ - `tpu_num_cores`: None
263
+ - `tpu_metrics_debug`: False
264
+ - `debug`: []
265
+ - `dataloader_drop_last`: False
266
+ - `dataloader_num_workers`: 0
267
+ - `dataloader_prefetch_factor`: None
268
+ - `past_index`: -1
269
+ - `disable_tqdm`: False
270
+ - `remove_unused_columns`: True
271
+ - `label_names`: None
272
+ - `load_best_model_at_end`: False
273
+ - `ignore_data_skip`: False
274
+ - `fsdp`: []
275
+ - `fsdp_min_num_params`: 0
276
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
277
+ - `fsdp_transformer_layer_cls_to_wrap`: None
278
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
279
+ - `deepspeed`: None
280
+ - `label_smoothing_factor`: 0.0
281
+ - `optim`: adamw_torch
282
+ - `optim_args`: None
283
+ - `adafactor`: False
284
+ - `group_by_length`: False
285
+ - `length_column_name`: length
286
+ - `ddp_find_unused_parameters`: None
287
+ - `ddp_bucket_cap_mb`: None
288
+ - `ddp_broadcast_buffers`: False
289
+ - `dataloader_pin_memory`: True
290
+ - `dataloader_persistent_workers`: False
291
+ - `skip_memory_metrics`: True
292
+ - `use_legacy_prediction_loop`: False
293
+ - `push_to_hub`: False
294
+ - `resume_from_checkpoint`: None
295
+ - `hub_model_id`: None
296
+ - `hub_strategy`: every_save
297
+ - `hub_private_repo`: None
298
+ - `hub_always_push`: False
299
+ - `gradient_checkpointing`: False
300
+ - `gradient_checkpointing_kwargs`: None
301
+ - `include_inputs_for_metrics`: False
302
+ - `include_for_metrics`: []
303
+ - `eval_do_concat_batches`: True
304
+ - `fp16_backend`: auto
305
+ - `push_to_hub_model_id`: None
306
+ - `push_to_hub_organization`: None
307
+ - `mp_parameters`:
308
+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
318
+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
322
+ - `use_liger_kernel`: False
323
+ - `eval_use_gather_object`: False
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+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: round_robin
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+
329
+ </details>
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+
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+ ### Training Logs
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+ | Epoch | Step | sts-eval_spearman_cosine |
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+ |:------:|:----:|:------------------------:|
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+ | 1.0 | 75 | -0.1137 |
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+ | 1.3333 | 100 | -0.1012 |
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+ | 2.0 | 150 | -0.0837 |
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+ | 2.6667 | 200 | -0.0694 |
338
+ | 3.0 | 225 | -0.0748 |
339
+ | 4.0 | 300 | -0.0701 |
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+
341
+
342
+ ### Framework Versions
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+ - Python: 3.11.13
344
+ - Sentence Transformers: 4.1.0
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+ - Transformers: 4.52.4
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+ - PyTorch: 2.6.0+cu124
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+ - Accelerate: 1.7.0
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+ - Datasets: 2.14.4
349
+ - Tokenizers: 0.21.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+
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+ #### Sentence Transformers
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+ ```bibtex
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
sentence-transformer-indonesia-final/config.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "architectures": [
3
+ "BertModel"
4
+ ],
5
+ "attention_probs_dropout_prob": 0.1,
6
+ "classifier_dropout": null,
7
+ "gradient_checkpointing": false,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 384,
11
+ "initializer_range": 0.02,
12
+ "intermediate_size": 1536,
13
+ "layer_norm_eps": 1e-12,
14
+ "max_position_embeddings": 512,
15
+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
18
+ "pad_token_id": 0,
19
+ "position_embedding_type": "absolute",
20
+ "torch_dtype": "float32",
21
+ "transformers_version": "4.52.4",
22
+ "type_vocab_size": 2,
23
+ "use_cache": true,
24
+ "vocab_size": 250037
25
+ }